Design and application of soft sensors in rural sewage treatment facilities
Recently, with the growing demand for water quality monitoring, soft measurement sensors have drawn public attention, which can overcome the drawbacks of high cost and long time needed in traditional measurement methods. In this study, a machine learning-based soft monitoring sensor was developed to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-11-01
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| Series: | Aqua |
| Subjects: | |
| Online Access: | http://aqua.iwaponline.com/content/72/11/2001 |
| _version_ | 1797429100234670080 |
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| author | Bing Li Siyuan Mao Tuo Tian Huaibin Bi Yuxin Tian Xueyan Ma Yong Qiu |
| author_facet | Bing Li Siyuan Mao Tuo Tian Huaibin Bi Yuxin Tian Xueyan Ma Yong Qiu |
| author_sort | Bing Li |
| collection | DOAJ |
| description | Recently, with the growing demand for water quality monitoring, soft measurement sensors have drawn public attention, which can overcome the drawbacks of high cost and long time needed in traditional measurement methods. In this study, a machine learning-based soft monitoring sensor was developed to simultaneously monitor four water quality indicators including COD, NH4+-N, NO3--N, PO43--P. Firstly, specialized experimental equipment and calibration methods were developed to generate a matching dataset that collected over 94,000 data points. Secondly, five models including Multiple Linear Regression, Ridge Regression, AdaBoost, Decision Tree Regression, and Bagging Regression were constructed and compared. The learning accuracy of the models ranged from 0.8860 to 0.9999, among which the predicted value of Bagging Regression is highly fit to the true value. Subsequently, the fuzzy grade method was adopted to reduce the prediction error and strike a balance between efficiency and accuracy. Finally, the designed soft sensor was used for real-time monitoring at three monitoring points in Changzhou, China from September to October 2020, and the results proved the feasibility of the soft sensor in practical application. This study provided a fast and accurate method for water quality measurement, which is of great significance for the management of rural sewage treatment facilities.
HIGHLIGHTS
The soft sensor based on machine learning is applied to the water quality monitoring of rural sewage treatment facilities.;
Designing of laboratory-level devices to obtain datasets for model training.;
Fuzzy classification method is introduced to analyze and process data to reduce errors and obtain comprehensive water quality evaluation results.; |
| first_indexed | 2024-03-09T09:08:18Z |
| format | Article |
| id | doaj.art-ace43a51643b4cc691d3cb2d10f356fd |
| institution | Directory Open Access Journal |
| issn | 2709-8028 2709-8036 |
| language | English |
| last_indexed | 2024-03-09T09:08:18Z |
| publishDate | 2023-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Aqua |
| spelling | doaj.art-ace43a51643b4cc691d3cb2d10f356fd2023-12-02T09:44:08ZengIWA PublishingAqua2709-80282709-80362023-11-0172112001201610.2166/aqua.2023.062062Design and application of soft sensors in rural sewage treatment facilitiesBing Li0Siyuan Mao1Tuo Tian2Huaibin Bi3Yuxin Tian4Xueyan Ma5Yong Qiu6 School of Energy and Environmental Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing, China School of Energy and Environmental Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing, China School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, China School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, China School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, China School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, China School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, China Recently, with the growing demand for water quality monitoring, soft measurement sensors have drawn public attention, which can overcome the drawbacks of high cost and long time needed in traditional measurement methods. In this study, a machine learning-based soft monitoring sensor was developed to simultaneously monitor four water quality indicators including COD, NH4+-N, NO3--N, PO43--P. Firstly, specialized experimental equipment and calibration methods were developed to generate a matching dataset that collected over 94,000 data points. Secondly, five models including Multiple Linear Regression, Ridge Regression, AdaBoost, Decision Tree Regression, and Bagging Regression were constructed and compared. The learning accuracy of the models ranged from 0.8860 to 0.9999, among which the predicted value of Bagging Regression is highly fit to the true value. Subsequently, the fuzzy grade method was adopted to reduce the prediction error and strike a balance between efficiency and accuracy. Finally, the designed soft sensor was used for real-time monitoring at three monitoring points in Changzhou, China from September to October 2020, and the results proved the feasibility of the soft sensor in practical application. This study provided a fast and accurate method for water quality measurement, which is of great significance for the management of rural sewage treatment facilities. HIGHLIGHTS The soft sensor based on machine learning is applied to the water quality monitoring of rural sewage treatment facilities.; Designing of laboratory-level devices to obtain datasets for model training.; Fuzzy classification method is introduced to analyze and process data to reduce errors and obtain comprehensive water quality evaluation results.;http://aqua.iwaponline.com/content/72/11/2001bagging regressionfuzzy gradingmachine learningprocess controlwater quality monitoring |
| spellingShingle | Bing Li Siyuan Mao Tuo Tian Huaibin Bi Yuxin Tian Xueyan Ma Yong Qiu Design and application of soft sensors in rural sewage treatment facilities Aqua bagging regression fuzzy grading machine learning process control water quality monitoring |
| title | Design and application of soft sensors in rural sewage treatment facilities |
| title_full | Design and application of soft sensors in rural sewage treatment facilities |
| title_fullStr | Design and application of soft sensors in rural sewage treatment facilities |
| title_full_unstemmed | Design and application of soft sensors in rural sewage treatment facilities |
| title_short | Design and application of soft sensors in rural sewage treatment facilities |
| title_sort | design and application of soft sensors in rural sewage treatment facilities |
| topic | bagging regression fuzzy grading machine learning process control water quality monitoring |
| url | http://aqua.iwaponline.com/content/72/11/2001 |
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